Being hampered by the difficulties in feature-space adaptation, the current tendency turns to explore the DA possibility in other spaces, including pixel (input) space and segmentation (output) space. The common idea of the pixel-space adaptations is to force the input images to look like from the same domain, thus decreasing the domain shift from the headstream. While segmentation-space approaches are based on the observation that the segmentation results usually share a significant amount of similarities on the spatial layout and local context. Currently, these two lines of work have produced leading results on the semantic segmentation task while the feature-space adaptation appears eclipsed in front of them.
Taking the DA task GTA5 [1]} \(\rightarrow \)  Cityscapes [2]} as an example, there is a big difference in segmentation accuracy between the feature-space and output-space adaptation method: \(29.2\%~vs~34.8\%\)  [3]} on VGG-16 [4]}, \(31.7\%~vs~37.0\%\)  [3]} on DRN-26 [6]}, and \(39.3\%~vs~41.4\%\)  [7]} on ResNet-101 [8]}, respectively.
The performance gap is so significant that it is justifiable the previous methods choose output-space adaptation as their first choice.
